We use a voxel resolution of \(128^3\)  of the SPARF dataset in most of the learning experiments and visualizations in this work, unless otherwise clearly stated. This choice is to reduce the computational cost of training the heavy pipeline and to facilitate the development of proper learning methods on SRFs. The input SRF is normalized with a fixed value of 10,0000 for the density and 10 for the colors, to ensure the distribution lies within -1 to 1. The Q-Gaussian std \(\sigma \)  is set to 0.444 (studied more in Section ). The strides for the SuRFNEt are all set 2, while the network depth \(l=3\)  modules. The batch size for training is 14 when A100 GPUs are used and 6 when V100 GPUs are used. The training saturates at 100 epochs. The optimizer used is AdamW [1]} with a learning rate of 0.01, a momentum of 0.9, a weight decay of \(1e-5\) , and a learning rate exponential decay rate of 0.99. The hyperparameters \(\lambda _{R}, \lambda _{\alpha }, \lambda _{\rho }\)  are all set independently to each class, where a different network is trained on each class separately. Most classes have \(\lambda _{\alpha } = 30.0 ,\lambda _{\rho }=1.0, \lambda _{R} \) . We did not prune the output sparse voxel as this leads to harming performance most of the time and increase the problem of vanishing gradients. The background color of the rendered images \(\mathcal {R}_{\phi } \left( \mathbf {F}(\mathcal {X})\right)\)  is masked out from the perceptual loss and the density component \(\alpha \)  of the output SRF is also not affected by the perceptual loss, as this can cause excessive densities around the object, leading to deteriorating the SRF output perceptuality. During training with the perceptual loss, three randomly selected images from three different \(\phi \)  as used as labels for the three rendered images from the output \(\mathcal {R}_{\phi } \left( \mathbf {F}(\mathcal {X})\right)\) . The SuRFNet is jointly predicting the density and radiance of spherical harmonics colors, but with different heads. More setup details can be found in the attached code and analyzed further in Section .
